Standard C84.1 of the American National Standards Institute (ANSI) specifies that service voltage levels should be maintained at ±5% for 120 volt nominal service voltage to customer meters, for a range of 114 volts to 126 volts. Generally, utilities are highly motivated to reduce system voltage in order to the use less fuel and/or to use fuel more efficiently, to reduce carbon dioxide emissions, and to prolong equipment life. However, because of system losses and compensation for peak demand periods, utilities tend to provide service on the upper end of the C84.1 requirement (i.e., around 126 volts).
Typical voltage and VAR (Volt-Ampere or Volt Amperes Reactance) control devices include, for example, transformers provided with tap changers for adjusting transformer output voltage, feeder meters within substations to monitor the particular phase voltages on the feeders, and capacitor banks to reduce nonproductive reactive power flows through the distribution network. Such control devices may be used, for example, in an attempt to minimize power loss without causing voltage violations.
Generally, a VAR is useless energy that is created by a phase shift between the voltage and current in a line. VARs are introduced into the system through capacitance and inductive loading generated by customer equipment, such as but not limited to electronics, heavy industrial equipment, and the like. To remove VAR, one either has to increase the amount of power generation or implement controls, e.g., capacitance banks, in the grid to compensate for the VARs and realign the voltage and current sine waves. A capacitance bank introduces a phase shift into the network to compensate for the shift between the voltage and current sine waves in an attempt to counteract the losses associated with VARs. However, capacitance banks are not necessarily effective to counteract all VAR, and the utility typically still generates extra power to make up for the VARs that are in the system.
In current control scenarios, there are multiple control devices on a distribution network that are used and monitored independently of one another. More particularly, the grid controls in use today typically lack two-way communication, switching in or out automatically based on supervisory control and/or data acquisition (SCADA) interaction. Accordingly, there is minimal coordination within the system when an adjustment is made (e.g., coordination at one transformer when a switching event occurs at another transformers in the same network). Additionally, even if these controls are implemented on the distribution side of the grid, utilities may not have access to collected event data, resulting in a system with very little transparency. As such, with data about individual control events, there is a limit to what can be initiated by utilities to make corrections that affect their end users. This uncoordinated scenario is not optimal.
There is therefore a need in the art for a process, device and system that deploys a plurality of sensors to the grid to monitor the effect of a switching event at the grid level on a real-time synchronized basis.